Triple

T9834133
Position Surface form Disambiguated ID Type / Status
Subject Marburg (Lahn) station E239058 entity
Predicate serves P98 FINISHED
Object city of Marburg E824439 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: city of Marburg | Statement: [Marburg (Lahn) station, serves, city of Marburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: city of Marburg
Context triple: [Marburg (Lahn) station, serves, city of Marburg]
  • A. city of Marburg chosen
    The city of Marburg is a historic university town in the German state of Hesse, known for its well-preserved medieval old town and the Philipps-Universität Marburg.
  • B. University of Marburg
    The University of Marburg is one of Germany’s oldest universities and a prominent research institution, particularly renowned for its contributions to the natural sciences and medicine.
  • C. Heidelberg
    Heidelberg is a historic university city in southwestern Germany renowned for its picturesque old town, castle ruins, and one of Europe’s oldest universities.
  • D. Heidelberg
    Heidelberg is a suburb of Melbourne, Australia, known for its historic role in Australian Impressionism and its location along the Yarra River.
  • E. Heidelberg
    Heidelberg is a South African town known for its historical significance and role as a regional service and commercial center.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69ca84e314108190978324a4bdb959f8 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdb3385054819094145c96204e3f0d completed April 2, 2026, 12:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1ead061388190abbed7eb29e8ea52 completed April 5, 2026, 4:53 a.m.
Created at: March 30, 2026, 8:32 p.m.